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Multi-Actor Markov Decision Processes

Part of: Operations research and management science

Published online by Cambridge University Press:  14 July 2016

Hyun-Soo Ahn  and
Rhonda Righter
Hyun-Soo Ahn*
Affiliation:
University of Michigan
Rhonda Righter*
Affiliation:
University of California, Berkeley
*
∗Postal address: Operations and Management Science, University of Michigan Business School, 701 Tappan Street, Ann Arbor, MI 48109-1234, USA. Email address: [email protected]
∗∗Postal address: Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA 94720, USA. Email address: [email protected]
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Abstract

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We give a very general reformulation of multi-actor Markov decision processes and show that there is a tendency for the actors to take the same action whenever possible. This considerably reduces the complexity of the problem, either facilitating numerical computation of the optimal policy or providing a basis for a heuristic.

Keywords

Markov decision processmultiarmed banditflexible server

MSC classification

Secondary: 90C40: Markov and semi-Markov decision processes 90B22: Queues and service
Type
Research Papers
Information
Journal of Applied Probability , Volume 42 , Issue 1 , March 2005 , pp. 15 - 26
Copyright
© Applied Probability Trust 2005 

References

Ahn, H.-S., Duenyas, I. and Lewis, M. E. (2002). The optimal control of a two-stage tandem queueing system with flexible servers. Prob. Eng. Inf. Sci. 16, 453–469.CrossRefGoogle Scholar
Andradöttir, S., Ayhan, H. and Down, D. G. (2001). Server assignment policies for maximizing the steady-state throughput. Manag. Sci. 47, 1421–1439.Google Scholar
Gittins, J. C. (1979). Bandit processes and dynamic allocation indices. J. R. Statist. Soc. B. 14, 148–177.Google Scholar
Harrison, J. M. (1975). Dynamic scheduling of a multiclass queue: discount optimality. Operat. Res. 23, 270–282.Google Scholar
Kaufman, D., Ahn, H.-S. and Lewis, M. E. (2004). On the introduction of agile, temporary workers into a tandem queueing system. Work in progress.Google Scholar
Klimov, G. P. (1974). Time-sharing service systems. I. J. Theory Prob. Appl. 19, 532–551.Google Scholar
Klimov, G. P. (1978). Time-sharing service systems. II. J. Theory Prob. Appl. 23, 314–321.CrossRefGoogle Scholar
Koole, K. and Righter, R. (2004). Resource allocation in grid computing. Work in progress.Google Scholar
Mandelbaum, A. and Reiman, M. I. (1998). On pooling in queueing networks. Manag. Sci. 44, 971–981.CrossRefGoogle Scholar
Sennott, L. I. (1999). Stochastic Dynamic Programming and the Control of Queueing Systems. John Wiley, New York.Google Scholar
Van Oyen, M. P., Gel, E. G. S. and Hopp, W. J. (2001). Performance opportunity for workforce agility in collaborative and noncollaborative work systems. IIE Trans. 33, 761–777.CrossRefGoogle Scholar
Vairaktarakis, G. L. (2003). The value of resource flexibility in the resource-constrained Job assignment problem. Manag. Sci. 49, 718–732.CrossRefGoogle Scholar
Weiss, G. and Pinedo, M. (1980). Scheduling tasks with exponential service times on non-identical processors to minimize various cost functions. J. Appl. Prob. 17, 187–202.Google Scholar